The goal of this project is triggering cell-type-specific behaviors with rationally designed nano-environments. Cell behavior critically depends on the cellular microenvironment, i.e. the physical and (bio)chemical properties of the material surrounding the cell. The PIs have demonstrated that nanostructures in the environment elicit a unique and universal response across cell types. The response to subcellular scale nano-environment can drive unique cell behaviors, including guidance over large distances, control with subcellular precision, and, when combined with other cell guidance cues, cell behavior that is controlled on multiple scales. The research will yield quantitative insights and predictive phase-field simulations that will be validated and improved in feedback between experiments and simulations. This feedback loop relies critically on controlled cellular experiments with advanced image analysis, cutting-edge 3D phase-field modeling, and machine learning to link experiments and simulations and forge a path towards predictive understanding of cell behavior in nano-environments. Precise control of cell behavior with nano-environments holds great promise for a broad range of biological and biomedical applications that require precise steering of the migration and behavior of cells and tissues. The research project will allow the PIs to develop guiding principles for the design of such nano-environments for specific tasks, which will enable these materials to be applied to a broad range of tasks that are beneficial to society through medical and other technologies. The PIs will train scientists in the use of the image-analysis and modeling software that will be developed, which will be freely available. This training will be offered in the form of week-long, intensive bootcamps. The PIs will also use the research to reach out to the general public. The results of the project will be broadly disseminated to the academic community through publications, conferences and workshops.
Esotaxis, the guidance of cytoskeletal dynamics by nanotopography, is a phenomenon that was discovered only recently by the PIs. It is a highly conserved phenomenon in mammalian cells that opens up novel, cell-type-specific and spatially precise control opportunities, but it is not yet well understood. This project will lead to a predictive understanding of esotaxis and how it can be harnessed with rationally designed nano-environments. This understanding will be achieved through an iterative cycle involving materials design and fabrication, validation through cellular imaging and advanced analysis, and tuning and extension of the three dimensional phase field simulations. Machine learning approaches will allow the team to determine which cellular characteristics are the most important for determining esotactic phenotypes, as well as well as to correlate nano-topographic features with specific esotactic behaviors. Starting with initial results from 3D phase-field simulations that exhibit qualitative agreement with key experimental predictions, the team expects to develop a quantitatively predictive model that, as a second goal, will also incorporate realistic cytoskeletal dynamics, and enable simultaneous control of cell functions on multiple scales. The third goal is to demonstrate cell type specific control in a biologically relevant model.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
